Author Identifier (ORCID)
Liang Wang: https://orcid.org/0000-0001-5339-7484
Abstract
Hyperuricemia (HUA) and gout result from imbalances in uric acid metabolism and are closely associated with the gut microbiota. Advanced analytical methods facilitate the exploration of microbiota complexity. In this study, 16S rRNA sequencing data from stool samples of 233 patients were thoroughly collected. Machine learning (ML) and Shapley Additive exPlanations (SHAP) interpretability algorithms were applied to identify core taxa and predict the metabolic functions. The results revealed that the high-contribution core taxa identified by SHAP in each group, such as Oscillospiraceae_UCG-005 and Rhodococcus provided the basis for ML prediction. Among the five classification models, Random Forest (RF) achieved the best diagnostic performance, with prediction accuracy ranging from 82 to 96%. Metabolic function predictions indicated that the purine metabolism pathway contributes the most to distinguishing gout from other groups. In sum, ML-based 16S rRNA sequencing reveals key gut microbiome biomarkers, aiding new diagnostic strategies for HUA and gout.
Document Type
Journal Article
Date of Publication
12-1-2025
Volume
25
Issue
1
PubMed ID
40640723
Publisher
Springer
School
Centre for Precision Health / School of Medical and Health Sciences
RAS ID
83607
Funders
Research Foundation for Advanced Talents of Guandong Provincial People’s Hospital (KY012023293 / Australian Commonwealth Government / University of Western Australia
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Comments
Tang, J., Tay, A. C. Y., & Wang, L. (2025). Interpretive prediction of hyperuricemia and gout patients via machine learning analysis of human gut microbiome. BMC Microbiology, 25. https://doi.org/10.1186/s12866-025-04125-x